Lecture 1: Introduction
Yale University
Staff
Econometrics vs. Statistics/Data Science
Econ 136
Field: Microeconometrics, Applied-Microeconometics;
Co-Editor of Journal of Applied Econometrics.
Fellow of the Econometric Society.
Founding member and former Director of the
International Association of Applied Econometrics.
Yale Economics Ph.d. student.
Field: Econometric Theory
Contact:
Staff
Econometrics vs. Statistics/Data Science
Econ 136
What is econometrics?
If statistics is the “science of learning from data,”
then what is data science?
Is data science another name for applied statistics?
Samuelson, Koopmans, and Stone (1954):
the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference
Staff
Econometrics vs. Statistics/Data Science
Econ 136
Is designed for students in:
Is designed to prepare students for graduate level courses.
Very different from Econ 117/123.
incorporates computationally intensive methods such as bootstrap and cross-validation,
incorporates aspects of data science.
The course is substantially more mathematically rigorous than typical undergraduate-level econometrics courses, and will make extensive use of:
Multivariate calculus
Linear Algebra
In this course, we will use R,
Advantages of R over other options? Disadvantages?
Conditional expectations and linear projections,
Causal Analysis,
Asymptotic Analysis,
Linear regression analysis, including model selection,
Bootstrap,
Instrumental Variables,
Limited Dependent Variable models.
Will relate to economic models of discrimination:
statistical- vs taste-based discrimination.
Econ 135: Introduction to Probability and Statistics, or
S&DS 241 and 242.
What if you haven’t taken Econ 135 or S&DS 241+242?
Lectures: Lectures will primarily use blackboard, will sometimes be accompanying handouts.
Labs: In-class labs where we live-code in R to analyze real data. The labs will be designed to directly help you with your problem sets.
You are expected to attend lectures and labs.
I will call on students.
| Assignments | Share of Course Grade |
|---|---|
| Online Quizzes | 10% |
| Problem Sets | 30% |
| Midterm | 25% |
| Final | 35% |
Will include primarily theoretical questions but also computational/empirical work.
Due dates are strict.1
The lowest problem set score will be dropped.
You may work in groups of up to four students on the problem sets.1
However, you must turn in your own assignment and indicate on your submission the other members of the group.
On Thursday, lecture will review rules for expected value and variance of random vectors.
On Thursday, lecture will review rules for expected value and variance of random vectors.
Optional Reading: Review Expectations and Variance
On Thursday, lecture will review rules for expected value and variance of random vectors.
Optional Reading: Review Matrix Algebra
First problem set assigned Thursday January 24, due Tuesday February 6.
First quiz goes live Friday February 2.
Econ 136: Lecture 1